Gender, institutions, and economic development: Findings and open research and policy issues
Bibliographic record
Abstract
Gender relations are a key institution governing important aspects of production and reproduction of societies. They are guided by formal institutions as well as informal norms and values. As this survey shows, there is great regional heterogeneity in gender inequality in formal and informal social institutions. The literature on long-term drivers of gender gaps suggests that those gender gaps are related to long-standing and regularly reproduced gender norms and values related to differences in women's economic opportunities and constraints. The paper also shows that these gender gaps not only affect gender equity but overall development outcomes such as economic growth and reductions in mortality. This is best documented in the case of gender gaps in education but there is also evidence for the negative effects gender of gaps in employment, political and economic empowerment, access to resources, and social institutions on development outcomes. The paper then shows that there has been a large and heterogeneous change in gender gaps. While gender gaps in education (and legal rights) have closed very rapidly, gender gaps in labor force participation, health, political participation, and time use have closed much less rapidly, and there has been virtually progress in reducing occupational and sectoral segregation, unexplained gender pay gaps, and violence against women. The paper presents some hypotheses that might explain this differential performance and also contribute to understanding regional dynamics, before pointing towards a forward-looking research agenda on better understanding the linkages between institutions and their change, gender inequality, and economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".